WebCSEA

WebCSEA performs cell-type specific enrichment analysis of gene sets using single-cell RNA sequencing (scRNA-seq) derived tissue-cell-type signatures to identify tissue- and cell-type-specific expression patterns relevant to complex traits and diseases.

Key Features:

• Extensive Database: Leverages a curated compendium of single-cell RNA sequencing (scRNA-seq) data across 111 panels encompassing 1,355 tissue-cell types (TCs) from 61 general tissues within 11 organ systems.
• Decoding Tissue-Specificity (deTS) Algorithm: Implements an adapted decoding tissue-specificity (deTS) algorithm to measure and assess enrichment of input gene sets in specific tissue-cell types.
• Permutation-Based Methodology: Applies a permutation-based approach to correct for biases from varying numbers of signature genes across TCs and to estimate cell-type specificity significance.
• Interactive Visualizations: Generates interactive and static heatmaps and visualizations profiling cell-type specificity across 1,355 TCs and summarizing results by organ system, developmental stage, and top-ranked tissues and cell types.

Scientific Applications:

• Complex Trait and Disease Interpretation: Identifies tissue-cell-type associations for gene sets derived from studies of human complex traits and diseases.
• Molecular Mechanism Investigation: Facilitates investigation of cell-type-specific expression patterns to infer molecular underpinnings of phenotypes.
• Cellular Heterogeneity Characterization: Enables characterization of cellular heterogeneity within human tissues using large-scale scRNA-seq-derived tissue-cell-type signatures.

Methodology:

Performs enrichment analysis using an scRNA-seq-derived signature compendium (111 panels, 1,355 TCs) with an adapted decoding tissue-specificity (deTS) algorithm and permutation-based significance estimation.